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To achieve near-continuous vertical observations of volatile organic compounds (VOCs) in the planetary boundary layer (PBL), multi-channel whole-air sampling equipment onboard an unmanned aerial vehicle (UAV) platform was developed in this study. The equipment consists of a multi-position solenoid valve and specially designed lightweight quartz sampling canisters. The canisters have little adsorption loss of VOCs and good inter-canister reproducibility. The 7 d recovery test shows that most VOC species (97 %) had a 1-week decay within 20 %. Online instruments for measuring O3, NO2, CO, SO2, and meteorological parameters are also integrated into the UAV platform. During one take-off and landing, the UAV platform can reach 800 m above the ground within 40 min and take whole-air samples at six heights. Vertical profiles of VOCs and trace gases during the evolution of the PBL in south-western China are successfully obtained by deploying the newly developed UAV system.

An orchard variable-rate sprayer applies the appropriate amount of plant protection products only where they are needed based on detection data from advanced sensors, a system that has attracted increasing attention. The latest developments in the detection unit, variable control unit, and signal-processing algorithm of the variable-rate sprayer are discussed. The detection of target position and volume is realized with an ultrasonic sensor, a laser scanning sensor, or other methods. The technology of real-time acquisition of foliage density, plant diseases and pests and their severity, as well as meteorological parameters needs further improvements. Among the three variable-flow-rate control units, pulse width modulation was the most widely used, followed by pressure-based, and variable concentration, which is preliminarily verified in the laboratory. The variable air supply control unit is tested both in the laboratory and in field experiments. The tree-row-volume model, the leaf-wall-area model, and the continuous application mode are widely used algorithms. Advanced research on a variable-rate sprayer is analyzed and future prospects are pointed out. A laser-based variable-rate intelligent sprayer equipped with pulse width modulation solenoid valves to tune spray outputs in real time based on target structures may have the potential to be successfully adopted by growers on a large scale in the foreseeable future. It will be a future research direction to develop an intelligent multi-sensor-fusion variable-rate sprayer based on target crop characteristics, plant diseases and pests and their severity, as well as meteorological conditions while achieving multi-variable control.

With a decadal long period (1998–2010) climate simulation using the Weather Research and Forecasting model at convection-permitting resolution (4 km) (WRF_CPM), the diurnal cycles of precipitation amount (PA), frequency (PF) and intensity (PI) and their related large-scale atmospheric circulations over eastern China are analyzed. The simulations are further compared against the CN05.1, CMORPH v1.0 and the ECMWF Re-Analysis Interim (ERAIN). Results show that WRF_CPM can reasonably represent the observed seasonal rainfall and the atmospheric circulations. As for the features at a sub-daily scale, WRF_CPM is superior at reproducing the diurnal amplitude of PF that is similar to PA in terms of the spatial distribution. Moreover, the diurnal peak timing of summer PF and PA over the three sub-regions, i.e., North China (NC), Yangtze-Huaihe River basin (YHR) and South China (SC), can be properly reproduced by WRF_CPM. The observed precipitation systems exhibit obvious eastward propagation from the Plateau to its downstream, which may be due to the solenoid circulations associated with the low-level anomalous wind and moisture convergence. However, they are almost overestimated by WRF_CPM and in turn causing overestimated precipitation along YHR. The early morning precipitation in WRF_CPM has a larger fraction than CMORPH, which is related to the overestimated nocturnal low-level jet. Whereas, due to the solar heating and the land-sea breezes, the late-afternoon precipitation peak is mainly located along the coasts of eastern China, which matches well with the vertical motion in WRF_CPM.

The Pearl River delta (PRD) region has experienced rapid economic development since the 1980s and has become one of the world’s largest industrial zones and metropolitan areas. Previous studies have shown that the sea-breeze circulation can contribute to pollutant transportation and convective initiation, so it is useful to study the dynamic structure of the sea-breeze circulation in the PRD region. Many researchers have focused on the effects of environmental factors, such as topography, urbanization, and background wind, on the sea breeze, but most focused only on case studies and did not quantify the characteristics of the sea-breeze circulation climatologically. In this study, a sea-breeze identification metric was defined to identify sea-breeze events from WRF simulation data of 2012 and quantify their characteristics, including their start time, end time, strength, height, frequency, pumping ability, and inland-penetrating distance. The results indicate that this method works well to identify and quantify the sea-breeze events of 2012. It is found that the solenoid term, the largest positive contributor to vorticity acceleration, is mostly modulated by the temperature gradient. Therefore, the frontogenesis of the sea-breeze front is discussed in this study. The result shows that offshore background wind that increases frontogenesis is favorable to the development of the sea breeze, but it also prevents it from propagating vertically and horizontally.

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area.

We describe the Airborne Mid-Infrared Cavity enhanced Absorption spectrometer (AMICA) designed to measure trace gases in situ on research aircraft using Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS). AMICA contains two largely independent and exchangeable OA-ICOS arrangements, allowing for the simultaneous measurement of multiple substances in different infrared wavelength windows tailored to scientific questions related to a particular flight mission. Three OA-ICOS setups have been implemented with the aim to measure OCS, CO2, CO, and H2O at 2050 cm−1; O3, NH3, and CO2 at 1034 cm−1; and HCN, C2H2, and N2O at 3331 cm−1. The 2050 cm−1 setup has been characterized in the laboratory and successfully used for atmospheric measurements during two campaigns with the research aircraft M55 Geophysica and one with the German HALO (High Altitude and Long Range Research Aircraft). For OCS and CO, data for scientific use have been produced with 5 % accuracy (15 % for CO below 60 ppb, due to additional uncertainties introduced by dilution of the standard) at typical atmospheric mixing ratios and laboratory-measured 1σ precision of 30 ppt for OCS and 3 ppb for CO at 0.5 Hz time resolution. For CO2, high absorption at atmospheric mixing ratios leads to saturation effects that limit sensitivity and complicate the spectral analysis, resulting in too large uncertainties for scientific use. For H2O, absorption is too weak to be measured at mixing ratios below 100 ppm. By further reducing electrical noise and improving the treatment of the baseline in the spectral retrieval, we hope to improve precision for OCS and CO, resolve the issues inhibiting useful CO2 measurements, and lower the detection limit for H2O. The 1035 and 3331 cm−1 arrangements have only partially been characterized and are still in development. Although both setups have been flown and recorded infrared spectra during field campaigns, no data for scientific use have yet been produced due to unresolved deviations of the retrieved mixing ratios to known standards (O3) or insufficient sensitivity (NH3, HCN, C2H2, N2O). The ∼100 kg instrument with a typical in-flight power consumption of about 500 VA is dimensioned to fit into one 19 in. rack typically used for deployment inside the aircraft cabin. Its rugged design and a pressurized and temperature-stabilized compartment containing the sensitive optical and electronic hardware also allow for deployment in payload bays outside the pressurized cabin even at high altitudes of 20 km. A sample flow system with two parallel proportional solenoid valves of different size orifices allows for precise regulation of cavity pressure over the wide range of inlet port pressures encountered between the ground and maximum flight altitudes. Sample flow of the order of 1 SLM (standard litre per minute) maintained by an exhaust-side pump limits the useful time resolution to about 2.5 s (corresponding to the average cavity flush time), equivalent to 500 m distance at a typical aircraft speed of 200 m s−1.

This study developed an efficient cultivation strategy for cabbage production in paddy fields. To address poor drainage, discarded coir substrates (CS) were reused and compared with conventional paddy soil (PS). Four irrigation levels (ETc140, ETc100, ETc60, and ETc0) were applied to both CS and PS to evaluate their interactive effects. An automated irrigation system was deployed, integrating a weather sensor and solenoid valves via a LoRa-based IoT network. Hourly ET0 was calculated based on Penman–Monteith in real time, and an irrigation event was triggered when cumulative ET0 reached 1 mm (CS) or 3 mm (PS). The automated irrigation system showed stable performance. Hourly ET0 estimates were 97% consistent with Korea Meteorological Administration data. The actual total irrigation depth (ID_actual) remained within 2% of the calculated depth (ID). Under moderate irrigation depths (ETc60 and ETc100), the reuse of CS significantly improved cabbage photosynthetic efficiency. Both CS-ETc60 and CS-ETc100 treatments maintained superior yield performance compared with other treatments. This integrated strategy not only offers a practical solution for improving water use efficiency but also enhances the multifunctional utilization of paddy fields, supporting the transition toward more sustainable agricultural practices.

The traditional continuous, quantitative spraying technology ignores the severity of pests, diseases and grasses, spatial distribution and other differences, resulting in low effective utilization of pesticides, environmental pollution and other problems. Variable-rate spray technology has become an important development direction in the field of precision agriculture by dynamically sensing crop canopy morphology, pest and disease distribution, and environmental parameters, adjusting the application amount in real time, and significantly improving pesticide utilization. In this study, we systematically review the core progress of variable-rate spray technology; focus on the technical system of information detection, spray volume model, and control system; analyze the current bottlenecks; and propose an optimization path to adapt to the complex agricultural conditions. At the level of information perception, LiDAR, machine vision, and multi-source sensor fusion technology constitute the main perception architecture, and infrared and ultrasonic sensors assist target recognition in complex scenes. In the construction of the spray volume model, models based on canopy volume, leaf area density, etc., are used to realize dynamic application decision by fusing equipment operating parameters, pest and disease levels, meteorological conditions, and so on. The control system takes the solenoid valve + PID control as the core program, and improves the response speed through PWM regulation and closed-loop feedback. The current technical bottlenecks are mainly concentrated in the sensor dynamic detection accuracy, model environmental adaptability, and the reliability of the execution parts. In the future, it is necessary to further promote anti-jamming multi-source heterogeneous sensor data fusion, multi-factor adaptive spray model development, lightweight edge computing deployment, and solenoid valve structural parameter optimization and other technical research, with a view to promoting the application of variable-rate spray technology to the field on a large scale and providing a theoretical reference and technological support for the green transformation of agriculture.

The continuous wave heavy ion beam linear accelerator at FRIB consists of 46 cryo-modules, housing 324 superconducting radio-frequency (SRF) resonators and 69 superconducting solenoids. The three linear accelerator (Linac) segments, designated LS1 to LS3, are arranged in the shape of a paper clip, with 4 superconducting dipole magnets in the curved segments. The SRF resonators are operated at 2 K, requiring a sub-atmospheric helium pressure, and the superconducting magnets are operated at 4.5 K. The design of the cryogenic transfer-lines for these Linac segments is complex and contains multiple process lines. Namely, these are the primary (4.5 K) supply, and return, sub-atmospheric return, 35 K shield supply and 55 K shield return. The shield return encloses the other process lines, thermally intercepting the ambient temperature heat load. Testing was conducted on the re-pressurization and liquid levels of the 24 cryo-modules of LS2’s 2 K system. System models were developed and then compared to the test data to characterize the static heat in-leak to the cryo-modules. Reasonable agreement was found between the validated models and preliminary measurements.

Farmland irrigation is an essential foundation for good crop growth, while traditional farmland irrigation techniques cannot fully consider the impact of factors such as natural precipitation and crop transpiration on crop growth, which can, to a certain extent, result in poor irrigation decisions and a complex farmland environment that cannot be monitored promptly, thereby reducing farmland production efficiency. This study designs a farmland irrigation control system based on a composite controller. Firstly, an irrigation control method is proposed to establish a prediction model for future rainfall and crop transpiration using historical meteorological data. The composite controller is designed based on the prediction model to realize an irrigation control operation with an irrigation value as the control quantity, a water and fertilizer machine, and a solenoid valve as the actuators. Secondly, an intelligent irrigation control cloud platform based on Java language is designed to monitor farm information and irrigation operation records in real-time to facilitate visual management. Finally, the prediction accuracy is high, based on the prediction model results, which can provide a specific reference basis. The superiority of the proposed controller is verified by simulation using MATLAB/Simulink. The results show that the proposed controller can be well suited for nonlinear control systems and has good control performance while ensuring high tracking accuracy, strong robustness, and fast convergence.